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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Improved Estimation of Leaf Chlorophyll Content from Non-Noon Reflectance Spectra of Wheat Canopies by Avoiding the Effect of Soil Background
摘要： Crop leaf chlorophyll content (LCC) is a valuable indicator for agronomists to make fertilization recommendation and can be estimated from canopy reflectance spectra. However, the estimation accuracy of LCC is often influenced by soil background. To alleviate the adverse effect of soil background, this study proposed to collect spectral measurements at non-noon (such as 14:00-16:00 local time) hours and evaluated the performance of these spectral measurements with experimental data and radiative transfer model. The results from the wheat experiment conducted at Rugao demonstrated that the canopy spectra measured at non-noon were less sensitive to the soil background compared with those collected at midday (such as 12:00), which improved the estimation accuracy (R2) for LCC from 0.71 to 0.77. A canopy radiative transfer model called 4SAIL-RowCrop was also used to validate the performance and feasibility of the non-noon measurement scheme. One thousand spectra with different combinations of LCC, soil reflectance, and canopy structure were simulated at three observation times (12:00, 14:00 and 16:00). The CIred-edge calculated from the canopy reflectance spectra simulated for 16:00 exhibited a higher correlation to LCC (R2 = 0.76) than that for 12:00 (R2 = 0.43). These consistent findings from experimental and modeled datasets suggested that the effect of soil background can be alleviated and the estimation accuracy of LCC can be improved by determining a proper timing of spectral observation.
关键词： Remote sensing,Wheat,Leaf chlorophyll content,Non-noon observation,Soil Background
更新于2025-09-11 14:15:04
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Wavelength selection of the multispectral lidar system for estimating leaf chlorophyll and water contents through the PROSPECT model
摘要： The estimation of leaf biochemical constituents is of high interest for the physiological and ecological applications of remote sensing. The multispectral lidar (MSL) system emerges as a promising active remote sensing technology with the ability to acquire both three-dimensional and spectral characteristics of targets. The detection wavelengths of the MSL system can be geared toward the specific application purposes. Therefore, it’s important to conduct the wavelength selection work to maximize the potential of the MSL system in vegetation monitoring. Traditional strategies of wavelength selection attempt to establish an empirical relationship between large quantities of observed reflectance and foliar biochemical constituents. By contrast, this study proposed to select wavelengths through the radiative transfer model PROSPECT. A five-wavelength combination was established to estimate leaf chlorophyll and water contents: 680, 716, 1104, 1882 and 1920 nm. The consistency of the wavelengths selected were tested by running different versions of PROSPECT model. Model inversion using simulated and experimental datasets showed that the selected wavelengths have the ability to retrieve leaf chlorophyll and water contents accurately. Overall, this study demonstrated the potential of the MSL system in vegetation monitoring and can serve as a guide in the design of new MSL systems for the application community.
关键词： Multispectral lidar,Wavelength selection,Leaf water content,Leaf chlorophyll content,PROSPECT model
更新于2025-09-04 15:30:14